410,572 research outputs found
Non-Parametric Approximations for Anisotropy Estimation in Two-dimensional Differentiable Gaussian Random Fields
Spatially referenced data often have autocovariance functions with elliptical
isolevel contours, a property known as geometric anisotropy. The anisotropy
parameters include the tilt of the ellipse (orientation angle) with respect to
a reference axis and the aspect ratio of the principal correlation lengths.
Since these parameters are unknown a priori, sample estimates are needed to
define suitable spatial models for the interpolation of incomplete data. The
distribution of the anisotropy statistics is determined by a non-Gaussian
sampling joint probability density. By means of analytical calculations, we
derive an explicit expression for the joint probability density function of the
anisotropy statistics for Gaussian, stationary and differentiable random
fields. Based on this expression, we obtain an approximate joint density which
we use to formulate a statistical test for isotropy. The approximate joint
density is independent of the autocovariance function and provides conservative
probability and confidence regions for the anisotropy parameters. We validate
the theoretical analysis by means of simulations using synthetic data, and we
illustrate the detection of anisotropy changes with a case study involving
background radiation exposure data. The approximate joint density provides (i)
a stand-alone approximate estimate of the anisotropy statistics distribution
(ii) informed initial values for maximum likelihood estimation, and (iii) a
useful prior for Bayesian anisotropy inference.Comment: 39 pages; 8 figure
On the robustness of the average power ratios in damping estimation: application in the structural health monitoring of composites beams
In composites structures, cracking, delamination will cause changes in the measured dynamic response of structure and so on experimentally modal parameters. Estimation of damping in structural control often poses a difficult problem especially using broadband experiments. If these estimations are faulty, it is difficult to propose a robust Structural Health Monitoring (SHM) algorithm. Recently H.P. Yin introduced the optimal power ratios damping estimator. A new theoretical basis of the bandwidth method for the damping estimation from frequency response functions (in case of a single degree of freedom system) has been proposed. The main goal of this paper is to study the robustness of this enhanced damping estimator on simulated signal (sampling frequency, Signal to Noise Ratio and damping level/density), and also compare its performance with industrial improved estimator like “Polymax” on experimental Frequency Response Functions (FRFs). The pole shifts would be studied as a change in the frequency-damping plane function of level and density of damage
Inverse Density as an Inverse Problem: The Fredholm Equation Approach
In this paper we address the problem of estimating the ratio
where is a density function and is another density, or, more generally
an arbitrary function. Knowing or approximating this ratio is needed in various
problems of inference and integration, in particular, when one needs to average
a function with respect to one probability distribution, given a sample from
another. It is often referred as {\it importance sampling} in statistical
inference and is also closely related to the problem of {\it covariate shift}
in transfer learning as well as to various MCMC methods. It may also be useful
for separating the underlying geometry of a space, say a manifold, from the
density function defined on it.
Our approach is based on reformulating the problem of estimating
as an inverse problem in terms of an integral operator
corresponding to a kernel, and thus reducing it to an integral equation, known
as the Fredholm problem of the first kind. This formulation, combined with the
techniques of regularization and kernel methods, leads to a principled
kernel-based framework for constructing algorithms and for analyzing them
theoretically.
The resulting family of algorithms (FIRE, for Fredholm Inverse Regularized
Estimator) is flexible, simple and easy to implement.
We provide detailed theoretical analysis including concentration bounds and
convergence rates for the Gaussian kernel in the case of densities defined on
, compact domains in and smooth -dimensional sub-manifolds of
the Euclidean space.
We also show experimental results including applications to classification
and semi-supervised learning within the covariate shift framework and
demonstrate some encouraging experimental comparisons. We also show how the
parameters of our algorithms can be chosen in a completely unsupervised manner.Comment: Fixing a few typos in last versio
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